Puromycin {datasets} | R Documentation |
The Puromycin
data frame has 23 rows and 3 columns of the
reaction velocity versus substrate concentration in an enzymatic
reaction involving untreated cells or cells treated with Puromycin.
Puromycin
This data frame contains the following columns:
treated
untreated
Data on the “velocity” of an enzymatic reaction were obtained by Treloar (1974). The number of counts per minute of radioactive product from the reaction was measured as a function of substrate concentration in parts per million (ppm) and from these counts the initial rate, or “velocity,” of the reaction was calculated (counts/min/min). The experiment was conducted once with the enzyme treated with Puromycin, and once with the enzyme untreated.
Bates, D.M. and Watts, D.G. (1988), Nonlinear Regression Analysis and Its Applications, Wiley, Appendix A1.3.
Treloar, M. A. (1974), Effects of Puromycin on Galactosyltransferase in Golgi Membranes, M.Sc. Thesis, U. of Toronto.
plot(rate ~ conc, data = Puromycin, las = 1, xlab = "Substrate concentration (ppm)", ylab = "Reaction velocity (counts/min/min)", pch = as.integer(Puromycin$state), col = as.integer(Puromycin$state), main = "Puromycin data and fitted Michaelis-Menten curves") ## simplest form of fitting the Michaelis-Menten model to these data fm1 <- nls(rate ~ Vm * conc/(K + conc), data = Puromycin, subset = state == "treated", start = c(Vm = 200, K = 0.05), trace = TRUE) fm2 <- nls(rate ~ Vm * conc/(K + conc), data = Puromycin, subset = state == "untreated", start = c(Vm = 160, K = 0.05), trace = TRUE) summary(fm1) summary(fm2) ## using partial linearity fm3 <- nls(rate ~ conc/(K + conc), data = Puromycin, subset = state == "treated", start = c(K = 0.05), algorithm = "plinear", trace = TRUE) ## using a self-starting model fm4 <- nls(rate ~ SSmicmen(conc, Vm, K), data = Puromycin, subset = state == "treated") summary(fm4) ## add fitted lines to the plot conc <- seq(0, 1.2, len = 101) lines(conc, predict(fm1, list(conc = conc)), lty = 1, col = 1) lines(conc, predict(fm2, list(conc = conc)), lty = 2, col = 2) legend(0.8, 120, levels(Puromycin$state), col = 1:2, lty = 1:2, pch = 1:2)